We propose to investigate improved algorithms for several challenges associated with chromosome conformation capture (3C) assays and related experiments. These recent high-throughput experiments give pairwise contact information between chromatin regions and have provided glimpses of the spatial organization of the genomes of several organisms. They have been used to computationally infer three-dimensional models of chromatin structure and to hypothesize functional spatial relationships among genomic features such as co-expressed genes, regulatory regions and their regulated genes, common breakpoint locations, and others. However, computational tools for structural modeling, relating function to structure, and for visualizing 3C data are still lacking. This proposal seeks to develop computational tools for several central 3C analysis tasks. In Aim 1, we propose coupling sampling with an optimization framework to model populations of chromatin structures that are consistent with 3C data. This is essential because 3C provides an average over different structures in millions of cells. In Aim 2, we devise techniques to find common and different structural features within these ensembles, comparing structures of different cell types (e.g. cancer vs. normal; lyphoblastoid vs. fibroblast) and better techniques t identify genomic loci that are statistically significantly spatially co-located. Finally, in Aim 3 e propose to develop a spatial genome browser that integrates both 1-d genomic annotations (genes, methylation, DNAase accessibility, etc.) with 3C spatial data. We will apply these techniques to quantifying the amount of cell-to-cell and cell-type variation in human, yeast, and mouse. Using improved populations of models, we will identify new instances of long-range regulation and explain existing postulated distal enhancer-promoter interactions. We will also correlate structure with eQTLs and GWAS-identified SNPs to explain the mechanism causing the eQTL and the effect of the SNP. Finally, we will search for relationships between co-expressed genes and spatial proximity. The techniques we propose will result in better structural models computed more efficiently and a better understanding of the relationships between structure and function.